Learning Manipulation Tasks in Dynamic and Shared 3D Spaces
This addresses automation needs in sectors with high-volume material handling, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the problem of automating pick-and-place operations in dynamic 3D environments with human factors by proposing a deep reinforcement learning strategy for dual-manipulators, resulting in an increase in cumulative reward when agents are further from human factors.
Automating the segregation process is a need for every sector experiencing a high volume of materials handling, repetitive and exhaustive operations, in addition to risky exposures. Learning automated pick-and-place operations can be efficiently done by introducing collaborative autonomous systems (e.g. manipulators) in the workplace and among human operators. In this paper, we propose a deep reinforcement learning strategy to learn the place task of multi-categorical items from a shared workspace between dual-manipulators and to multi-goal destinations, assuming the pick has been already completed. The learning strategy leverages first a stochastic actor-critic framework to train an agent's policy network, and second, a dynamic 3D Gym environment where both static and dynamic obstacles (e.g. human factors and robot mate) constitute the state space of a Markov decision process. Learning is conducted in a Gazebo simulator and experiments show an increase in cumulative reward function for the agent further away from human factors. Future investigations will be conducted to enhance the task performance for both agents simultaneously.